Optimal two-dimensional roughness for transition delay in high-speed boundary layer

Jahanbakhshi, Reza; Zaki, Tamer A.

doi:10.1017/jfm.2023.523

Cited by 2 publications

(3 citation statements)

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“…We assume that the upstream flow, at the start of the domain of interest, is a linear superposition of a laminar state and fifty two instability waves with frequencies and integer azimuthal wavenumbers (f, k); details are provided in the "Methods" section. Thirteen frequencies are considered f ∈ [50, 75, ..., 350] kHz, and four integer azimuthal wavenumbers k ∈ [0, 20,40,60] . The control vector is therefore a fifty-two dimensional, c ∈ R 52×1 , unknown input and searching for its optimal value can be challenging since the system is nonlinear and chaotic.…”

Section: Measurements and Unknown Inputsmentioning

confidence: 99%

“…. , 350] kHz and integer azimuthal wavenumbers k ∈ [0, 20,40,60] . The data assimilation searches for the vector c = [.…”

Section: Compressible Navier-stokesmentioning

confidence: 99%

“…The shortcoming of computations, even direct numerical simulations (DNS) that resolve all the flow scales, is that they invoke modeling assumptions, most importantly idealized inflow/boundary conditions that determine the downstream flow, including laminar-to-turbulence transition 16 . Such idealizations are a main source of uncertainty since changing the oncoming waves can quantitatively, and also qualitatively, change the transition process [16][17][18][19][20] .…”

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confidence: 99%

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ML for fast assimilation of wall-pressure measurements from hypersonic flow over a cone

Morra,

Meneveau,

Zaki

2024

Sci Rep

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Data assimilation (DA) integrates experimental measurements into computational models to enable high-fidelity predictions of dynamical systems. However, the cost associated with solving this inverse problem, from measurements to the state, can be prohibitive for complex systems such as transitional hypersonic flows. We introduce an accurate and efficient deep-learning approach that alleviates this computational burden, and that enables approximately three orders of magnitude computational acceleration relative to variational techniques. Our method pivots on the deployment of a deep operator network (DeepONet) as an accurate, parsimonious and efficient meta-model of the compressible Navier–Stokes equations. The approach involves two main steps, each addressing specific challenges. Firstly, we reduce the computational load by minimizing the number of costly direct numerical simulations to construct a comprehensive dataset for effective supervised learning. This is achieved by optimally sampling the space of possible solutions. Secondly, we expedite the computation of high-dimensional assimilated solutions by deploying the DeepONet. This entails efficiently navigating the DeepONet’s approximation of the cost landscape using a gradient-free technique. We demonstrate the successful application of this method for data assimilation of wind-tunnel measurements of a Mach 6, transitional, boundary-layer flow over a 7-degree half-angle cone.

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Section: Measurements and Unknown Inputsmentioning

confidence: 99%

“…. , 350] kHz and integer azimuthal wavenumbers k ∈ [0, 20,40,60] . The data assimilation searches for the vector c = [.…”

Section: Compressible Navier-stokesmentioning

confidence: 99%

mentioning

confidence: 99%

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ML for fast assimilation of wall-pressure measurements from hypersonic flow over a cone

Morra,

Meneveau,

Zaki

2024

Sci Rep

Self Cite

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Ensemble variational method with adaptive covariance inflation for learning neural network-based turbulence models

Luo,

Zhang,

2024

Physics of Fluids

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This work introduces an ensemble variational method with adaptive covariance inflation for learning nonlinear eddy viscosity turbulence models where the Reynolds stress anisotropy is represented with tensor-basis neural networks. The ensemble-based method has emerged as an important alternative to data-driven turbulence modeling due to its merit of non-derivativeness. However, the training accuracy of the ensemble method can be affected by the linearization assumption and sample collapse issue. Given these difficulties, we introduce the hybrid ensemble variational method, which inherits the merits of the ensemble method in non-derivativeness and the variational method in nonlinear analysis. Moreover, a covariance inflation scheme is proposed based on convergence states to alleviate the detrimental effects of sample collapse. The capability of the ensemble variational method in model learning is tested for flows in a square duct, flows over periodic hills, and flows around the S809 airfoil, with increasing complexity in the training data from direct observation to sparse indirect observation. Our results show that the ensemble variational method can learn relatively accurate neural network-based turbulence models in scenarios of small ensemble size and sample variances, compared to the ensemble Kalman method. It highlights the superiority of the ensemble variational method in practical applications, since small ensemble sizes can reduce computational costs, and small sample variance can ensure the training robustness by avoiding nonphysical samples of Reynolds stresses.

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Optimal two-dimensional roughness for transition delay in high-speed boundary layer

Cited by 2 publications

References 57 publications

ML for fast assimilation of wall-pressure measurements from hypersonic flow over a cone

ML for fast assimilation of wall-pressure measurements from hypersonic flow over a cone

Ensemble variational method with adaptive covariance inflation for learning neural network-based turbulence models

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